diff --git a/RBF_tf.py b/RBF_tf.py
new file mode 100644
index 0000000..fb6d95c
--- /dev/null
+++ b/RBF_tf.py
@@ -0,0 +1,55 @@
+import tensorflow as tf
+
+class RBFLayer(tf.keras.layers.Layer):
+    """ RBF Layer using TF Subclassing API.
+
+    ...
+
+    Attributes
+    ----------
+    n_centers : int
+        The number of RBF centers.
+    input_dim : int
+        The dimensions of the RBF centers.
+
+    Methods
+    -------
+    call(inputs):
+        TF call method to implement forward pass.
+    """
+    def __init__(self, n_centers, input_dim=3):
+        """ Constructs RBF centers and standard deviations as trainable weights.
+
+        Parameters
+        ----------
+            n_centers : int
+                The number of RBF centers.
+            input_dim : int
+                The dimensions of the RBF centers. Default of 3 for Kp, Ki, Kd   
+        """
+        super().__init__()
+        self.n_centers = n_centers
+        self.centers = self.add_weight(shape=(n_centers, input_dim), 
+                                       initializer="random_normal",
+                                       trainable=True,)
+        self.sigmas = self.add_weight(shape=(n_centers,),
+                                      initializer="ones",
+                                      trainable=True,)
+
+    def call(self, inputs):
+        """ Find likelihood of inputs under Gaussian distribution given center and 
+        standard deviation. 
+
+        Parameters
+        ----------
+            inputs : tensor
+                The points in space to evaluate the Gaussian.
+
+        Returns
+        -------
+        Height of Gaussian curve at inputs.          
+        """
+        distances = tf.norm(tf.expand_dims(inputs, axis=1) - self.centers, axis=2) 
+        rbf_output = tf.exp(-tf.square(distances) / (2 * tf.square(self.sigmas))) 
+        return rbf_output
+